Artificial intelligence facial expression recognition is applicable to many fields, including human-computer interactions, safe driving, smart monitoring, surveillance, and medicine.
AI these days does a fairly good job of recognizing faces. But facial expression recognition with artificial intelligence is complicated and requires elaborate neural networks that demand a lot of training and are computationally expensive.
A new convolutional neural network (CNN) has been developed to address the problem by a research team led by Dr. Jia Tian from Jilin Engineering Normal University in China. His team concentrated on getting an optimal balance between the training speed, memory usage, and recognition accuracy of the model.
“The model we developed is particularly effective for facial expression recognition when using small sample datasets. The next step in our research is to further optimize the model’s architecture and achieve an even better classification performance,”
Among the differences between typical CNN models and the one envisioned by the researchers was the use of depth-wise separable convolutions. Convolutions are the core operation executed at each layer of a convolutional neural network.
Where a depth-wise separable convolution differs from the standard type is that it processes different channels (such as RGB) of the input image independently and combines the results at the end.
By blending this type of convolution with a technique called pre-activated residual blocks, the proposed model was able to process input facial expressions in a coarse-to-fine manner. In this way, the team greatly reduced the computational cost and the necessary number of parameters to be learned by the system for accurate classification.
“We managed to obtain a model with good generalization ability with as little as 58,000 parameters,”
72 Percent Accurate
The researchers tested their new model through a comparison of its facial expression recognition performance with that of other reported models in a classroom setting. They trained and tested all models using a popular dataset called the Extended Cohn-Kanade dataset, which contains over 35,000 labeled images of faces expressing common emotions.
The results were encouraging, with the model developed by Tian’s team exhibiting the highest accuracy (72.4%) with the least number of parameters.
Reference: Jia Tian et al, Facial expression recognition in classroom environment based on improved Xception model, Journal of Electronic Imaging (2022). DOI: 10.1117/1.JEI.31.5.051416
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